GSE55235

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

Whole-study status Not suitable

Dataset does not meet the current suitability threshold.

Whole-study downloads — these do not apply your sample requirements.

Selected cohort requirements

Samples meeting selected requirements: 0 cases / 0 controls

View matching samples

20 case / 10 control samples elsewhere in the study have incomplete or conflicting evidence; they are not included above

Patient pairs verified in indexed sample metadata: 0

A case/control comparison meeting these requirements is not confirmed.

Counts describe samples, not independent patients. Pair verification uses explicit subject identifiers in indexed sample metadata. Zero verified pairs does not establish that the study is unpaired; reported matched-normal design alone is insufficient.

Summary

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

Overall design

Synovial tisssue from healthy joints, OA joints, and RA joints.

Source Evidence

4 sources
Source Contribution Status
GEO

Original dataset metadata, sample metadata, source links, and GEO data availability flags.

Sample records
30 GSM samples
Primary source
recount3

Planned trusted source for processed count availability.

No match found
Expression Atlas

Curated expression experiment linkage and source URL.

Expression Atlas accession
E-GEOD-55235
Available
PubMed

Primary GEO citations and related PubMed articles that mention this dataset.

21 linked publications 21 DOIs

Primary publication

  1. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Arthritis research & therapy, 2014

Related publications

  1. The Integration of Bioinformatics with Experimental Validation Has Uncovered Shared Molecular Characteristics Between Sarcopenia and Osteoarthritis. Calcified tissue international, 2026
  2. Identification of potential key efferocytosis-related genes in rheumatoid arthritis using machine learning. Clinical rheumatology, 2026
  3. Aquaporin-1 stabilizes β-catenin to promote NLRP3 inflammasome-mediated pyroptosis in rheumatoid arthritis. Apoptosis : an international journal on programmed cell death, 2026
  4. CD27 Neutralization Attenuates Rheumatoid Arthritis by Suppressing. Biochemical genetics, 2026
  5. G protein-coupled receptor-related genes matrix metallopeptidase 9 and tachykinin precursor 1 promote osteoarthritis progression and influence the immune microenvironment. International journal of biological macromolecules, 2025
  6. Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration. Journal of orthopaedics, 2026
  7. Celastrol suppresses bone destruction in rheumatoid arthritis by inhibiting ALOX5 expression in macrophages via the NF-κB pathway. Scientific reports, 2025
  8. Elucidation of hub cross-talk genes as potential biomarkers and drug prediction for chronic periodontitis and rheumatoid arthritis: A bioinformatics analysis. Medicine, 2025
  9. Identification of differential expressed genes common to rheumatoid arthritis and osteoporosis based on GEO database. Medicine, 2025
  10. Integrative transcriptomic analysis identifies synovium-derived biomarkers for OA-related synovitis and builds a validated diagnostic nomogram. Medicine, 2026
  11. Identification of Autophagy-Associated Molecular Signatures in Osteoarthritis Revealed by Integrative Multi-Omics and Experimental Validation. Chemical biology & drug design, 2026
  12. STAT1 transcriptionally activates CXCL13 to promote cell inflammation and proliferation and inhibit cell apoptosis in rheumatoid arthritis fibroblast-like synoviocytes. Journal of orthopaedic surgery and research, 2026
  13. Programmed cell death-related gene S100A9 promotes macrophage M1 polarization and chondrocyte apoptosis in rheumatoid arthritis. Biology direct, 2026
  14. [Identification of efferocytosis-related genes in osteoarthritis and prediction of traditional Chinese medicines based on bioinformatics and machine learning]. Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026
  15. Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics. PloS one, 2026
  16. KIF11 promotes rheumatoid arthritis pathogenesis by activating M1 macrophage polarization and promoting inflammatory cytokine secretion. PloS one, 2026
  17. Bmal1 Attenuates Collagen-Induced Arthritis by Regulating Macrophage Polarization via Sirt1. Journal of inflammation research, 2026
  18. Immune cells with senescence-related transcriptional signatures orchestrate the inflammatory continuum in osteoarthritis synovium: a single-cell and machine learning study. Frontiers in immunology, 2026
  19. Identification and validation of SUMOylation-related key genes for osteoarthritis through integration of single-cell, bulk RNA sequencing and animal model experiments. Frontiers in medicine, 2026
  20. Mendelian Randomization and Transcriptome Analyses Reveal Important Roles for CEBPB and CX3CR1 in Osteoarthritis. Bioengineering (Basel, Switzerland), 2025
Available

Metadata Quality Components

Analysis Readiness

Ready after review
97% DE readiness

Contrast completeness

25/25 pts

Both case and control groups are present in the proposed grouping.

Minimum group size

20/20 pts

Each contrast group has at least 2 samples.

Processed data availability

20/20 pts

Processed data is marked available in the GEO index.

Sample decision clarity

15/15 pts

All samples are assigned to a case or control group.

Blocking warnings

7/10 pts

Warnings are present, but none are critical blockers.

Metadata normalization

10/10 pts

Metadata quality is high enough to support downstream handoff.

Blockers

No readiness blockers detected.

Next Steps

  • Verify the proposed sample assignments and case-versus-control comparison against the GEO metadata before using them in DESeq2 or another differential-expression tool.
  • Confirm processed count files and sample identifiers match the exported metadata.

Analysis Preparation Checklist

5/6 ready

Group labels reviewed

Needs review

Siftome proposed case and control labels, but no reviewer confirmation is stored yet.

Open the sample grouping CSV and confirm group labels before analysis.

Minimum sample count met

Ready

Both groups meet the minimum of 2 samples.

Confirm whether the cohort is large enough for the intended statistical analysis.

Processed counts available

Ready

Processed data availability is marked true in the GEO index.

Verify that downloaded processed files contain usable count or expression identifiers.

Ambiguous samples handled

Ready

No review-needed or unassigned samples are present in the proposed grouping.

Review the grouping table for biologically meaningful differences that the automated checks may not detect.

Exclusion decisions recorded

Ready

No hard-excluded samples were detected by the current rules.

Record any manual exclusions in the downstream analysis notes.

Source traceability preserved

Ready

The dataset and sample accessions can be traced back to GEO.

Keep GEO accessions with exported metadata.

Bioinformatician Review

Bioinformatician review required

Ask a bioinformatician to review grouping, count availability, exclusions, and design before DE.

Assay context may require expert handling before DESeq2-style analysis.

Why This Is Ranked Here

Tumor vs normal
Not suitable Dataset does not meet the current suitability threshold.

Applied Review Decisions

No active review decisions affect this ranking.

Derived Classifications

  • Assay differs from search intent Microarray Search intent expects bulk RNA-seq. Assay came from Normalization rules.
  • Disease context Rheumatoid arthritis Disease context came from Normalization rules.
  • Likely case group Osteoarthritis 20 samples
  • Likely control group Healthy control 10 samples

Intent Interpretation

  • Downgrade or exclusion reason Downgraded assay: microarray cohorts are separated from the RNA-seq intent. Derived from current-intent ranking reasons.

Ranking Facts

Supporting evidence

  • Comparison: Disease/model vs control. Specimen: Tissue. Comparison suitability: 0%.
  • Interpreted disease metadata: Rheumatoid arthritis.
  • Processed data is available for downstream review.
  • External evidence: Expression Atlas curated experiment link is available.
  • External evidence: PubMed links 21 publications; 21 DOIs available: PMID 24690414, PMID 40885352, PMID 40958328, PMID 41007174, PMID 41209768.
  • Publication context: Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.. Arthritis research & therapy, 2014. PMID 24690414. DOI 10.1186/ar4526.
  • Publication context: G protein-coupled receptor-related genes matrix metallopeptidase 9 and tachykinin precursor 1 promote osteoarthritis progression and influence the immune microenvironment.. International journal of biological macromolecules, 2025. PMID 40885352. DOI 10.1016/j.ijbiomac.2025.147208.
  • Publication context: Elucidation of hub cross-talk genes as potential biomarkers and drug prediction for chronic periodontitis and rheumatoid arthritis: A bioinformatics analysis.. Medicine, 2025. PMID 40958328. DOI 10.1097/MD.0000000000044358.
  • Publication context: 18 additional linked PubMed publications are available.

Cautions

  • Downgraded assay: microarray cohorts are separated from the RNA-seq intent.
  • Disease/control design without a tumor/normal comparison is outside tumor/normal triage.
  • Microarray expression context downgraded below primary bulk RNA-seq for the default tissue ranking.
  • Overall score capped because comparison suitability is weak for the selected search intent.
  • Overall score capped because microarray cohorts should be compared separately from RNA-seq cohorts.
  • Comparison suitability score is below the Phase 1 recommended threshold.

Dataset Warnings

Normalized Fields

104 fields

Sample Group Preview

30 rows shown
Show Hide
Study totals: Case 20 | Control 10 | Unassigned 0 | Excluded 0 | Ambiguous 0 30 rows shown
Group Role Accession Title Source / characteristics Reason
Osteoarthritis Case GSM1332211 Synovial tissue from osteoarthritic joint 1 (OA_1) Synovial tissue from osteoarthritic joint Disease state: Osteoarthritis; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332212 Synovial tissue from osteoarthritic joint 2 (OA_2) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332213 Synovial tissue from osteoarthritic joint 3 (OA_3) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332214 Synovial tissue from osteoarthritic joint 4 (OA_4) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332215 Synovial tissue from osteoarthritic joint 5 (OA_5) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332216 Synovial tissue from osteoarthritic joint 6 (OA_6) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332217 Synovial tissue from osteoarthritic joint 7 (OA_7) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332218 Synovial tissue from osteoarthritic joint 8 (OA_8) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332219 Synovial tissue from osteoarthritic joint 9 (OA_9) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Synovial tissue isolated from osteoarthritic joint Case GSM1332220 Synovial tissue from osteoarthritic joint 10 (OA_10) Synovial tissue from osteoarthritic joint Disease state: Synovial tissue isolated from osteoarthritic joint; Tissue: Synovial tissue isolated from joint Explicit characteristic comparison arm detected.
Rheumatoid arthritis Case GSM1332221 Synovial tissue from rheumatoid arthritis joint 1 (RA_1) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332222 Synovial tissue from rheumatoid arthritis joint 2 (RA_2) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332223 Synovial tissue from rheumatoid arthritis joint 3 (RA_3) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332224 Synovial tissue from rheumatoid arthritis joint 4 (RA_4) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332225 Synovial tissue from rheumatoid arthritis joint 5 (RA_5) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332226 Synovial tissue from rheumatoid arthritis joint 6 (RA_6) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332227 Synovial tissue from rheumatoid arthritis joint 7 (RA_7) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332228 Synovial tissue from rheumatoid arthritis joint 8 (RA_8) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332229 Synovial tissue from rheumatoid arthritis joint 9 (RA_9) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Rheumatoid arthritis Case GSM1332230 Synovial tissue from rheumatoid arthritis joint 10 (RA_10) Synovial tissue from rheumatoid arthritis joint Disease state: Rheumatoid arthritis; Tissue: Synovial tissue isolated from joint Case marker detected.
Healthy control Control GSM1332201 Synovial tisssue from healthy joint 1 (ND_1) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332202 Synovial tisssue from healthy joint 2 (ND_2) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332203 Synovial tisssue from healthy joint 3 (ND_3) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332204 Synovial tisssue from healthy joint 4 (ND_4) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332205 Synovial tisssue from healthy joint 5 (ND_5) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332206 Synovial tisssue from healthy joint 6 (ND_6) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332207 Synovial tisssue from healthy joint 7 (ND_7) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332208 Synovial tisssue from healthy joint 8 (ND_8) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332209 Synovial tisssue from healthy joint 9 (ND_9) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.
Healthy control Control GSM1332210 Synovial tisssue from healthy joint 10 (ND_10) Synovial tissue from healthy joint Disease state: Healthy control; Tissue: Synovial tissue isolated from joint Sample-local control marker detected.

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